{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "from plotly_resampler import unregister_plotly_resampler\n",
    "\n",
    "from neuralprophet import NeuralProphet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_metrics_plot(metrics):\n",
    "    # Deactivate the resampler since it is not compatible with kaleido (image export)\n",
    "    unregister_plotly_resampler()\n",
    "\n",
    "    # Plotly params\n",
    "    prediction_color = \"#2d92ff\"\n",
    "    actual_color = \"black\"\n",
    "    line_width = 2\n",
    "    xaxis_args = {\"showline\": True, \"mirror\": True, \"linewidth\": 1.5, \"showgrid\": False}\n",
    "    yaxis_args = {\n",
    "        \"showline\": True,\n",
    "        \"mirror\": True,\n",
    "        \"linewidth\": 1.5,\n",
    "        \"showgrid\": False,\n",
    "        \"rangemode\": \"tozero\",\n",
    "        \"type\": \"log\",\n",
    "    }\n",
    "    layout_args = {\n",
    "        \"autosize\": True,\n",
    "        \"template\": \"plotly_white\",\n",
    "        \"margin\": go.layout.Margin(l=0, r=10, b=0, t=30, pad=0),\n",
    "        \"font\": dict(size=10),\n",
    "        \"title\": dict(font=dict(size=10)),\n",
    "        \"width\": 1000,\n",
    "        \"height\": 200,\n",
    "    }\n",
    "\n",
    "    metric_cols = [col for col in metrics.columns if not (\"_val\" in col or col == \"RegLoss\" or col == \"epoch\")]\n",
    "    fig = make_subplots(rows=1, cols=len(metric_cols), subplot_titles=metric_cols)\n",
    "    for i, metric in enumerate(metric_cols):\n",
    "        fig.add_trace(\n",
    "            go.Scatter(\n",
    "                y=metrics[metric],\n",
    "                name=metric,\n",
    "                mode=\"lines\",\n",
    "                line=dict(color=prediction_color, width=line_width),\n",
    "                legendgroup=metric,\n",
    "            ),\n",
    "            row=1,\n",
    "            col=i + 1,\n",
    "        )\n",
    "        if f\"{metric}_val\" in metrics.columns:\n",
    "            fig.add_trace(\n",
    "                go.Scatter(\n",
    "                    y=metrics[f\"{metric}_val\"],\n",
    "                    name=f\"{metric}_val\",\n",
    "                    mode=\"lines\",\n",
    "                    line=dict(color=actual_color, width=line_width),\n",
    "                    legendgroup=metric,\n",
    "                ),\n",
    "                row=1,\n",
    "                col=i + 1,\n",
    "            )\n",
    "        if metric == \"Loss\":\n",
    "            fig.add_trace(\n",
    "                go.Scatter(\n",
    "                    y=metrics[\"RegLoss\"],\n",
    "                    name=\"RegLoss\",\n",
    "                    mode=\"lines\",\n",
    "                    line=dict(color=actual_color, width=line_width),\n",
    "                    legendgroup=metric,\n",
    "                ),\n",
    "                row=1,\n",
    "                col=i + 1,\n",
    "            )\n",
    "    fig.update_xaxes(xaxis_args)\n",
    "    fig.update_yaxes(yaxis_args)\n",
    "    fig.update_layout(layout_args)\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "DIR = \"~/github/neural_prophet\"\n",
    "DATA_DIR = os.path.join(DIR, \"tests\", \"test-data\")\n",
    "PEYTON_FILE = os.path.join(DATA_DIR, \"wp_log_peyton_manning.csv\")\n",
    "AIR_FILE = os.path.join(DATA_DIR, \"air_passengers.csv\")\n",
    "YOS_FILE = os.path.join(DATA_DIR, \"yosemite_temps.csv\")\n",
    "ENERGY_PRICE_DAILY_FILE = os.path.join(DATA_DIR, \"tutorial04_kaggle_energy_daily_temperature.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(ENERGY_PRICE_DAILY_FILE)\n",
    "df[\"temp\"] = df[\"temperature\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<neuralprophet.forecaster.NeuralProphet at 0x7f7fdfeb9690>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = NeuralProphet(\n",
    "    n_forecasts=7,\n",
    "    n_changepoints=0,\n",
    "    yearly_seasonality=True,\n",
    "    weekly_seasonality=True,\n",
    "    daily_seasonality=False,\n",
    "    n_lags=14,\n",
    ")\n",
    "m.add_lagged_regressor(\"temp\", n_lags=3)\n",
    "m.add_future_regressor(\"temperature\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.932% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n",
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n",
      "WARNING - (NP.forecaster.fit) - When Global modeling with local normalization, metrics are displayed in normalized scale.\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.924% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n",
      "INFO - (NP.config.init_data_params) - Setting normalization to global as only one dataframe provided for training.\n",
      "INFO - (NP.config.set_auto_batch_epoch) - Auto-set batch_size to 32\n",
      "INFO - (NP.config.set_auto_batch_epoch) - Auto-set epochs to 179\n",
      "WARNING - (NP.config.set_lr_finder_args) - Learning rate finder: The number of batches (41) is too small than the required number                     for the learning rate finder (228). The results might not be optimal.\n"
     ]
    },
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       "Finding best initial lr:   0%|          | 0/228 [00:00<?, ?it/s]"
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    "df_train, df_test = m.split_df(df=df, freq=\"D\", valid_p=0.1)\n",
    "\n",
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   "execution_count": 34,
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       "{'MAE_val': 5.427692413330078,\n",
       " 'RMSE_val': 6.7127461433410645,\n",
       " 'Loss_val': 0.00758162559941411,\n",
       " 'RegLoss_val': 0.0,\n",
       " 'epoch': 178,\n",
       " 'MAE': 5.914531707763672,\n",
       " 'RMSE': 7.906580448150635,\n",
       " 'Loss': 0.007893726229667664,\n",
       " 'RegLoss': 0.0}"
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     "execution_count": 34,
     "metadata": {},
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   "execution_count": 35,
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       "      MAE_val  RMSE_val  Loss_val  RegLoss_val  epoch       MAE     RMSE  \\\n",
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   "execution_count": 36,
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     "text": [
      "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.932% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.932% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n"
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       "Predicting: 41it [00:00, ?it/s]"
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      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n"
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    "forecast = m.predict(df)"
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   "execution_count": 38,
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       "                          'range': [2014-11-10 00:00:00, 2019-03-13 00:00:00],\n",
       "                          'showline': True,\n",
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       "                          'showline': True,\n",
       "                          'title': {'text': 'ds'},\n",
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       "                          'linewidth': 1.5,\n",
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       "                          'showline': True,\n",
       "                          'title': {'text': 'ds'},\n",
       "                          'type': 'date'},\n",
       "               'yaxis': {'anchor': 'x',\n",
       "                         'domain': [0.875, 1.0],\n",
       "                         'linewidth': 1.5,\n",
       "                         'mirror': True,\n",
       "                         'rangemode': 'normal',\n",
       "                         'showline': True,\n",
       "                         'title': {'text': 'Trend'}},\n",
       "               'yaxis2': {'anchor': 'x2',\n",
       "                          'domain': [0.7, 0.825],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'rangemode': 'tozero',\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'yearly seasonality'}},\n",
       "               'yaxis3': {'anchor': 'x3',\n",
       "                          'domain': [0.525, 0.65],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'rangemode': 'tozero',\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'weekly seasonality'}},\n",
       "               'yaxis4': {'anchor': 'x4',\n",
       "                          'domain': [0.35, 0.475],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'rangemode': 'tozero',\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'AR (7)-ahead'}},\n",
       "               'yaxis5': {'anchor': 'x5',\n",
       "                          'domain': [0.175, 0.3],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'rangemode': 'tozero',\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'Lagged Regressor \"temp\" (7)-ahead'}},\n",
       "               'yaxis6': {'anchor': 'x6',\n",
       "                          'domain': [0.0, 0.125],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'rangemode': 'tozero',\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'Additive Future Regressors'}}}\n",
       "})"
      ]
     },
     "execution_count": 38,
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       "                         'linewidth': 1.5,\n",
       "                         'mirror': True,\n",
       "                         'range': [2014-10-27 00:00:00, 2018-10-09 00:00:00],\n",
       "                         'showline': True,\n",
       "                         'title': {'text': 'ds'},\n",
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       "                          'domain': [0.0, 1.0],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'range': [2016-12-14 00:00:00, 2018-01-18 00:00:00],\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'Day of year'}},\n",
       "               'xaxis3': {'anchor': 'y3',\n",
       "                          'domain': [0.0, 1.0],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'range': [-8, 175],\n",
       "                          'showline': True,\n",
       "                          'tickmode': 'array',\n",
       "                          'ticktext': [Sunday, Monday, Tuesday, Wednesday,\n",
       "                                       Thursday, Friday, Saturday, Sunday, Sunday],\n",
       "                          'tickvals': [0, 24, 48, 72, 96, 120, 144, 168, 192],\n",
       "                          'title': {'text': 'Day of week'}},\n",
       "               'xaxis4': {'anchor': 'y4',\n",
       "                          'domain': [0.0, 1.0],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'range': [-1, 16],\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'AR lag number'}},\n",
       "               'xaxis5': {'anchor': 'y5',\n",
       "                          'domain': [0.0, 1.0],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'range': [0, 4],\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'Lagged Regressor \"temp\" lag number'}},\n",
       "               'xaxis6': {'anchor': 'y6',\n",
       "                          'domain': [0.0, 1.0],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'Additive future regressor name'}},\n",
       "               'yaxis': {'anchor': 'x',\n",
       "                         'domain': [0.875, 1.0],\n",
       "                         'linewidth': 1.5,\n",
       "                         'mirror': True,\n",
       "                         'rangemode': 'normal',\n",
       "                         'showline': True,\n",
       "                         'title': {'text': 'Trend'}},\n",
       "               'yaxis2': {'anchor': 'x2',\n",
       "                          'domain': [0.7, 0.825],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'rangemode': 'normal',\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'Seasonality: yearly'}},\n",
       "               'yaxis3': {'anchor': 'x3',\n",
       "                          'domain': [0.525, 0.65],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'rangemode': 'normal',\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'Seasonality: weekly'}},\n",
       "               'yaxis4': {'anchor': 'x4',\n",
       "                          'domain': [0.35, 0.475],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'rangemode': 'normal',\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'AR weight (7)-ahead'}},\n",
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       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'rangemode': 'normal',\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'Lagged Regressor \"temp\" weight (7)-ahead'}},\n",
       "               'yaxis6': {'anchor': 'x6',\n",
       "                          'domain': [0.0, 0.125],\n",
       "                          'linewidth': 1.5,\n",
       "                          'mirror': True,\n",
       "                          'rangemode': 'normal',\n",
       "                          'showline': True,\n",
       "                          'title': {'text': 'Additive future regressor weight'}}}\n",
       "})"
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     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
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   ],
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